A stable symmetrization of the linear systems arising in interior-point methods for solving linear programs is introduced. A comparison of the condition numbers of the resulting interiorpoint linear systems with other commonly used approaches indicates that the new approach may be best suitable for an iterative solution. It is shown that there is a natural generalization of this symmetrization ...

A self-adaptive fitness formulation is presented for constrained evolutionary optimization. The method has been formulated to ensure that slightly infeasible solutions with a low objective function value remain fit. This is seen as a benefit in solving highly constrained problems that have solutions on one or more of the constraint bounds. In contrast, solutions furthest from the constraint bou...

The artificial bee colony (ABC) algorithm is inspired by the behavior of honey bees. It is a relatively new optimization algorithm that has been proved competitive with conventional biology-inspired algorithms. The IABC algorithm is used, with the differential evolution (DE) algorithm added to the new solution search equation of ABC, to improve convergence speed. The IABC adopts the reward-base...

Under the competitive environments, power companies may calculate optimal power flow (OPF) in many occasions: especially for such real time applications as contingency analysis and congestion management. However, caused by congestions, etc., it is not guaranteed that OPF has the feasible solution in every calculation case. This paper proposes a new optimal power flow calculation model applicabl...

We propose two line search primal-dual interior-point methods that approximately solve a sequence of equality constrained barrier subproblems. To solve each subproblem, our methods apply a modified Newton method and use an `2-exact penalty function to attain feasibility. Our methods have strong global convergence properties under standard assumptions. Specifically, if the penalty parameter rema...

We describe simple and exact duals, and certificates of infeasibility and weak infeasibility in conic linear programming which do not rely on any constraint qualification, and retain most of the simplicity of the Lagrange dual. In particular, some of our infeasibility certificates generalize the row echelon form of a linear system of equations, and the “easy” proofs – as sufficiency of a certif...

Predicate constraint solving technique is an important method of automatic test data generation. By analyzing the properties and disadvantages of predicate constraint solving technique, three theorems are proposed and proved. Based on them, a new approach on automatic test data generation is presented. The linear predicate on a given path is used directly to construct the linear constraint syst...

Multistage stochastic linear programming (MSLP) is a powerful tool for making decisions under uncertainty. A deteministic equivalent of MSLP is a large-scale linear program with nonanticipativity constraints. Recently developed infeasible interior point methods are used to solve the resulting linear program. Technical problems arising from this approach include rank reduction and computation of...

In complex environment with hybrid terrain, different regions may have different terrain. Path planning for robots in such environment is an open NP-complete problem, which lacks effective methods. The paper develops a novel global path planning method based on common sense and evolution knowledge by adopting dual evolution structure in culture algorithms. Common sense describes terrain informa...